Summary

Project summary

Goals:

Findings:

Data Overview

Twitter data

Table of data

Here is a sample of the type of twitter information we obtained.

created_at tweet_id full_text user_id user_location geo_type geo_coordinates language retweet_count favorite_count lat lon
Sat May 11 17:08:11 +0000 2019 1.127259e+18 Oh, wow! This @dunecoffee Kenya Gitura is juicy. Brewed perfectly at French Press. @ The French Press https://t.co/lNWAInlyN2 328220132 Santa Barbara, CA Point c(34.42238857, -119.70336554) en 0 0 34.42239 -119.7034
Thu Dec 03 18:41:32 +0000 2015 6.724858e+17 Some people in these theory classes will bumble on for 5 minutes before they admit they don’t really know what they’re saying. 270102282 Los Angeles, CA Point c(34.41321134, -119.8505349) en 0 2 34.41321 -119.8505
Thu Feb 04 09:38:47 +0000 2016 6.951797e+17 When all the stars align and #openingnight hits. You just smile when #santabarbara shines so… https://t.co/cxPgBVXgxU 45503009 Santa Barbara, CA Point c(34.42431, -119.70685) en 0 0 34.42431 -119.7069
Thu Dec 22 19:49:55 +0000 2016 8.120224e+17 The more you go within, the greater the out feels and looks AND the less you crave for… https://t.co/EiV9J1EPkX 45188474 Santa Barbara, CA. Point c(34.42139387, -119.61753992) en 0 0 34.42139 -119.6175
Thu Mar 05 15:17:30 +0000 2015 5.735026e+17

Back at it in 2 days! Stoked for this Friday’s show! mixmagmagazine @flosstradamus

#jarphotography… https://t.co/mnF66Dywgi 		1048777038 NA Point c(34.41570782, -119.8532751) en 0 0 34.41571 -119.8533
Sat Jul 02 16:58:17 +0000 2016 7.492861e+17 Flight of the Concords! #thisishowweparty #rocknroll @ Santa Barbara… https://t.co/0NDYQcQMmC 243076702 93274-sho! Point c(34.43503, -119.69376606) en 0 0 34.43503 -119.6938
Sat Apr 11 12:58:51 +0000 2015 5.868761e+17 Too early in a the morning! Looks like a “let’s go do something day” 2723393101 NA Point c(34.4100693, -119.8585918) en 0 1 34.41007 -119.8586
Sun Dec 18 03:50:51 +0000 2016 8.103315e+17 Cobb: You create the world of the dream, you bring the subject into that dream, and they fill it… https://t.co/7J5TlEQAxn 320495620 Santa Barbara, CA Point c(34.4244007, -119.78778133) en 0 0 34.42440 -119.7878
Wed Sep 27 01:25:24 +0000 2017 9.128506e+17 I’m at Kyle’s Kitchen in Santa Barbara, Calif https://t.co/ftvOMEa4e3 30830688 Santa Barbara, CA Point c(34.418392, -119.70044) en 0 1 34.41839 -119.7004
Mon Dec 31 20:35:41 +0000 2018 1.079839e+18 I just began a hiking workout using #Endomondo. Peptalk me now @ https://t.co/HQX4TIHGlK 49580724 Irvine, CA Point c(34.516697, -119.793259) en 0 0 34.51670 -119.7933

Caveats

Required crimson hexagon access

Maps

Interactive with cluster markers

As you zoom in on the map, clusters will disaggregate. You can click on blue points to see the tweet.

Tweet density

This is log-transformed. There is a single coordinate that has over 11,000 tweets reported across all years. It is near De La Vina between Islay and Valerio. There is nothing remarkable about this site so I assume it is the default coordinate when people tag “Santa Barbara” generally. The coordinate is 34.4258, -119.714.

Identifying tourists and locals

If the user has self-identified their location as somewhere in the Santa Barbara area, they are designated a local. This includes Carpinteria, Santa Barbara, Montecito, Goleta, Gaviota and UCSB. For the remainder, we use the number of times they have tweeted from Santa Barbara within a year to designate user type. If someone has tweeted across more than 2 months in the same year from Santa Barbara, they are identified as a local. This is consistent with how Eric Fischer determined tourists in his work. This is not fool-proof and there are instances were people visit and tweet from Santa Barbara more than two months a year, especially if they are visiting family or live within a couple hours driving distance.

There are 26408 tweets from tourists and 56468 tweets from locals.

Nature-based dictionary

Looking at where people are tweeting from

General patterns across the city

Map of locals vs users

Map of nature based vs non nature based

Specifically parks and protected areas

CPAD

Time

Timeline of tweets

Initial hypothesis was identifying spikes in nature-based tweets around three significant events: - Refugio oil spill in 2015 - Thomas fire in 2017 - Debris flow in 2018

Word clouds

top 100 words for locals vs tourist. And we could do this in space. At sterns wharf what are people tweeting about? At Elings, what are locals tweeting about?

Maybe in word clouds we can see some changes due to natural events

All of SB

By area

Sentiment Analysis

Summary

Lessons learned

Data is harder to find

Future research

Looking at different scale areas

There might be an interesting comparison between rural-suburban-urban areas. We hypothseize that the tourist/local alignment would split in urban areas, maybe aligned in suburban (like SB) and maybe not exist in rural.

Proportion of words that are nature based tells you how people. In Santa Barbara, there will be a lot of nature-based sense of place. In Manhattan, we wouldn’t expect to see nature based ones so much.

In a blog piece we can pose questions that we couldn’t answer but stuff like “can proportion of tourists/locals in place engagement tell us anything”.

Could compare % nature based tweets in SB to other areas. If we did this across the whole state, what proportion% are nature based? Maybe on average its just 5%.

Where and why do locals and tourists overlap in their use of area. SB seems to have a high alignment of tourists/locals, which may be helpful for local policy. Maybe places with distinct differences in how tourists/locals use places.

Look at cities of different coastal sizes rural - small town - urban - mega city. Could see how tourists/locals patterns differentiate across scale.

Is there a threshold of tourists where locals don’t go anymore?

In areas where we see both tourists and locals engaging, what characteristics do we see?

Quantifying transitions between rural to city.